chemical_formula_hill
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string | chemical_formula_anonymous
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list | elements_ratios
list | nelements
int32 | nsites
int32 | cell
list | positions
list | pbc
list | dimension_types
list | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | software
string | method
string | adsorption_energy
float64 | atomic_forces
list | atomization_energy
float64 | cauchy_stress
list | cauchy_stress_volume_normalized
bool | electronic_band_gap
float64 | electronic_band_gap_type
string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
string | property_object_last_modified
timestamp[ns] | property_object_hash
string | property_object_id
string | configuration_metadata
string | configuration_metadata_id
string | configuration_labels
list | configuration_names
list | configuration_dataset_ids
list | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
list | dataset_description
string | dataset_elements
list | dataset_nelements
int32 | dataset_nproperty_objects
int64 | dataset_nconfigurations
int32 | dataset_nsites
int64 | dataset_adsorption_energy_count
int64 | dataset_atomic_forces_count
int64 | dataset_atomization_energy_count
int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
int64 | dataset_last_modified
timestamp[ns] | dataset_dimension_types
list | dataset_nperiodic_dimensions
list | dataset_publication_year
string | dataset_total_elements_ratios
list | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
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Ge4
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Ge
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A
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|
MD_2127439833971556448789806
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PO_9560937189612565296687308
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|
MD_6209077217691384825807183
| null |
[
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] |
[
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"DS_jy3ylaf48xg3_0",
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] | 2023-12-05T20:21:07 |
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|
CO_1231832234782232076400667
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
GeSb
|
GeSb
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AB
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[
32,
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[
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|
MD_2127439833971556448789806
| 2024-08-16T15:22:43 |
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|
PO_4536543393184609492627715
|
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|
MD_2476909026557004540199065
| null |
[
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] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:03 |
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|
CO_1236569633605149579221529
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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1
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] |
[
3
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2023
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[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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[
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"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:09 |
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CO_1238686276172454622991871
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
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2023
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[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge44Sb44Te110
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Ge2Sb2Te5
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[
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"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
12428847628415892533773766421489776565531019999211386382579210504515705026225509741651585024485246622135983010711280067112661661659427854740343136370828576
|
CO_1242884762841589253377376
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
|
[
0.23625529319304173,
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0.545149030509906
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge80Sb20Te110
|
Ge8Sb2Te11
|
A11B8C2
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|
MD_2127439833971556448789806
| 2024-08-16T14:44:35 |
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PO_3634197105973903525026725
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|
MD_1045673044544388728978859
| null |
[
"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1243352352160989866913982
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge54Sb36Te108
|
Ge3Sb2Te6
|
A6B3C2
|
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| 2024-08-16T15:23:26 |
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PO_1259278112150140531292710
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MD_1159896415082430456140656
| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_1248660649100797867364684
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
]
] |
[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge27Sb54Te108
|
GeSb2Te4
|
A4B2C
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| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:03 |
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|
CO_1248901504534424262635205
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
|
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge104Sb26Te144
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Ge52Sb13Te72
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A72B52C13
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MD_4944613370450812909744964
| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_1249519740244972306059825
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge20Sb82Te144
|
Ge10Sb41Te72
|
A72B41C10
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_1250116194285847229316291
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb6
|
Sb
|
A
|
[
51,
51,
51,
51,
51,
51
] |
[
"Sb"
] |
[
1
] | 1 | 6 |
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[
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],
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PBE
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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MD_2127439833971556448789806
| 2024-08-16T15:33:24 |
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PO_5523454288310092831459877
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{"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "conv_ele_cryst", "hash": "11376588209332849124881644127347225818768470654784049131706689891424440782678255600298876432210759186988575066593177855023956768212279145568357977314864761", "id": "MD_1137658820933284912488164"}
|
MD_1137658820933284912488164
| null |
[
"main_gst_gap_22_pbesol_cryst_conv_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_conv_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_conv_ele_cryst"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_1252861345056940956373724
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
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[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge54Sb36Te108
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Ge3Sb2Te6
|
A6B3C2
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|
MD_6823662732873767234495495
| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1253324166330126981779731
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
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] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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MD_6240288368831179278655173
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[
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
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[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_1262204737182641778034268
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GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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1,
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3
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2023
|
[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge95Te95
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_1271288873208784766379561
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
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GeSb4Te7
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A7B4C
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|
MD_6235621281652257067515806
| null |
[
"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
12762031883908632982671177078654131329178953502253813488094550684647614684535789323327754326812755667161969070964072783926496312235259711496510550448388586
|
CO_1276203188390863298267117
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb76Te114
|
Sb2Te3
|
A3B2
|
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MD_1959771366508673057861790
| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_1281991867033802713931263
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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1,
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] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Te54
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Te
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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MD_2127439833971556448789806
| 2024-08-16T15:10:22 |
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PO_9263889514116251903155736
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{"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"}
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MD_1048234111509269510462403
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_1286214101314929648277370
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
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3
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2023
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[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb72Te108
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Sb2Te3
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A3B2
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MD_2127439833971556448789806
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PO_6213517821571823325107839
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MD_5467819520593778606574092
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[
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[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
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GST_GAP_22_refitted
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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MD_2127439833971556448789806
| 2024-08-16T14:20:49 |
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|
MD_1318897586789642758477079
| null |
[
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[
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"DS_jy3ylaf48xg3_0",
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|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
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"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge80Sb20Te110
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Ge8Sb2Te11
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A11B8C2
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MD_2127439833971556448789806
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MD_1217429169592300569428003
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GST_GAP_22_refitted
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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MD_2127439833971556448789806
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MD_2476909026557004540199065
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[
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[
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CO_1294614563954232715148601
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GST_GAP_22_refitted
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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10.60732/164f9a70
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MD_2127439833971556448789806
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PO_1205366490706092759279267
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MD_6515008115902207792427764
| null |
[
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] |
[
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CO_1297026483429352790732404
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
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] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge44Sb44Te110
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Ge2Sb2Te5
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A5B2C2
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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MD_2127439833971556448789806
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GST_GAP_22_refitted
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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MD_1406383406495818635985055
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[
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GST_GAP_22_refitted
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[
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"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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MD_2127439833971556448789806
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MD_4056174476448141592851415
| null |
[
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] |
[
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"DS_jy3ylaf48xg3_0",
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] | 2023-12-05T20:21:03 |
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CO_1316638758337043955631654
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge3Te3
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GeTe
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AB
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52
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[
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1,
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PBE
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MD_2127439833971556448789806
| 2024-08-16T15:29:19 |
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PO_6695052212637023280077249
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|
MD_2416211705896189987138528
| null |
[
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] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
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] | 2023-12-05T20:21:04 |
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|
CO_1321828880933707273059746
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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|
[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb6Te6
|
SbTe
|
AB
|
[
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51,
51,
51,
52,
52,
52,
52,
52,
52
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[
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MD_2127439833971556448789806
| 2024-08-16T15:29:22 |
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{"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "MP", "hash": "4275741023574137123122185753339649270607065339385900977771305811638412127363335787375676253456683463212131702163180486046814111828381999308143207348792451", "id": "MD_4275741023574137123122185"}
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MD_4275741023574137123122185
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[
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"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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CO_1324208686445589940438700
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
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"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge28Sb56Te112
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GeSb2Te4
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A4B2C
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MD_3471349848201599019655889
| null |
[
"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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|
CO_1325516429733314134811449
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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MD_2127439833971556448789806
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PO_4754075399526913936047125
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|
MD_1256966458646417390670398
| null |
[
"main_gst_gap_22_pbesol_iter_2_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_2_standard_iter",
"refitted_gst_gap_22_pbe_iter_2_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
13305872184391702331303358887818136878067170630024138139647368957683348538522076606334260385834435316871503297833798157487744384627242391807476254327618232
|
CO_1330587218439170233130335
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge108Te108
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GeTe
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AB
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| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_1334500549580010992919782
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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MD_2127439833971556448789806
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MD_1137658820933284912488164
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[
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[
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GST_GAP_22_refitted
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[
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"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge4Sb18Te32
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Ge2Sb9Te16
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A16B9C2
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MD_2127439833971556448789806
| 2024-08-16T14:14:08 |
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PO_1215633906764829799694268
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MD_7583248193253721435988354
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[
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[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_1409639809317159073156003
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GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
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PO_9285891707445927226585230
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MD_1406383406495818635985055
| null |
[
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[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_1480719412754453218356097
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
GeSb
|
GeSb
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AB
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[
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51
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[
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CASTEP
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PBE
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MD_2127439833971556448789806
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PO_1264715658641674551702753
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MD_2476909026557004540199065
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[
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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[
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge144Te144
|
GeTe
|
AB
|
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[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_1571652275875361659422195
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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1,
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] |
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3
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2023
|
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge48Sb32Te96
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Ge3Sb2Te6
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A6B3C2
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MD_1231133561391289073953192
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GST_GAP_22_refitted
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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MD_2127439833971556448789806
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PO_7351958817387580354861985
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MD_1137658820933284912488164
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[
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GST_GAP_22_refitted
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge80Sb20Te110
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Ge8Sb2Te11
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A11B8C2
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_1709599902346171961953537
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge48Sb32Te96
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Ge3Sb2Te6
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A6B3C2
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|
MD_6618865922658743579828373
| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
1770606074577208524160511936307213459774185549463116480073169973751164106810132231393678369766366595631047248610918375792386935271441877108264568599367842
|
CO_1770606074577208524160511
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
|
GeSb4Te7
|
A7B4C
|
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| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_1774428098453611039755696
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb96Te144
|
Sb2Te3
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A3B2
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PO_8430487437643841088275298
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MD_6830794917602741578894269
| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_1846282381386739365493149
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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GST_GAP_22_refitted
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[
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] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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"Te"
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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|
MD_6823662732873767234495495
| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1987090731245041384545801
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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3
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2023
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge18Sb18Te45
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Ge2Sb2Te5
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A5B2C2
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CASTEP
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PBE
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MD_2127439833971556448789806
| 2024-08-16T15:10:15 |
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PO_5241607151955487315529679
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|
MD_1833062707632126152463528
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_1989508179740457021075229
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
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[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005
|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge54Sb36Te108
|
Ge3Sb2Te6
|
A6B3C2
|
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MD_2127439833971556448789806
| 2024-08-16T14:36:41 |
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PO_3373045089925907167709389
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|
MD_7551892920841746894557062
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
1997075688930224244308614460290537630215475507619226750476057034935811130561099636456315891989749827601951851158527581468424875147109711608011689146871277
|
CO_1997075688930224244308614
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge44Sb44Te110
|
Ge2Sb2Te5
|
A5B2C2
|
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MD_2127439833971556448789806
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[
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GST_GAP_22_refitted
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[
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"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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MD_2127439833971556448789806
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PO_1030443125552359045105859
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MD_6515008115902207792427764
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[
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[
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"DS_jy3ylaf48xg3_0",
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] | 2023-12-05T20:21:03 |
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CO_2026923934521327918714661
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GST_GAP_22_refitted
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[
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"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge2Sb2Te5
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Ge2Sb2Te5
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A5B2C2
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MD_2127439833971556448789806
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MD_3736747359295474668976008
| null |
[
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[
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"DS_jy3ylaf48xg3_0",
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] | 2023-12-05T20:21:04 |
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CO_2055737022097030732976746
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
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"Te"
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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GeTe
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GeTe
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AB
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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MD_2127439833971556448789806
| 2024-08-16T15:20:16 |
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PO_9670630785316091692420545
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{"config-type": "cryst", "masses": [72.63, 127.6], "sub-config": "rhom_GeTe", "hash": "6240288368831179278655173368742685790632375324962132005821718706446864372084663405911025033538700741186128949108475768232718859484959809394658838888233988", "id": "MD_6240288368831179278655173"}
|
MD_6240288368831179278655173
| null |
[
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[
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"DS_jy3ylaf48xg3_0",
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CO_2060160550182273600051542
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
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"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge43Sb44Te108
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Ge43Sb44Te108
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A108B44C43
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|
MD_1338399383027260508638836
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_rs_anti",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs_anti",
"refitted_gst_gap_22_pbe_cryst_spc_rs_anti"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
2165761157767063061443948114425606836908258478364346280248929385325236311067706055715272964279973761663245793091019826120176716983163005285656284470384370
|
CO_2165761157767063061443948
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge144Te144
|
GeTe
|
AB
|
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[
"Ge",
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[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_2225443052418972520162559
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
]
] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
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GeSb4Te7
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A7B4C
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MD_2127439833971556448789806
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MD_6352484376044340676090156
| null |
[
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"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
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[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_2255250195644643973285284
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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CASTEP
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] | true | null | null | -30,882.237328 | null | 0.971254 | 2.497385 |
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|
MD_2127439833971556448789806
| 2024-08-16T14:40:08 |
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PO_4953183115830081954626773
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|
MD_1048234111509269510462403
| null |
[
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"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_2272044848027311134845476
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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1,
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[
3
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2023
|
[
0.23625529319304173,
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge48Sb32Te96
|
Ge3Sb2Te6
|
A6B3C2
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CASTEP
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PBE
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| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:09 |
2286284522507695914688449037489216029453989989092934747840221907435071061957412890386399017360799570470668569695396976229621952720696869751269489022361447
|
CO_2286284522507695914688449
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge104Sb26Te144
|
Ge52Sb13Te72
|
A72B52C13
|
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"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_2398306154971516108977875
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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] |
[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge27Sb54Te108
|
GeSb2Te4
|
A4B2C
|
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true,
true,
true
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[
1,
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| 1 |
CASTEP
|
PBE
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MD_8033838607892980577709491
| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:03 |
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|
CO_2402637663898465415358691
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Te54
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Te
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A
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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MD_2127439833971556448789806
| 2024-08-16T15:25:51 |
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{"config-type": "cryst", "masses": [127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "spc_ele_cryst", "hash": "10482341115092695104624036464431580998332388409744383351399034763727849966709074052587122925035349033589465704752437318101099927297656895760288257555499527", "id": "MD_1048234111509269510462403"}
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MD_1048234111509269510462403
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_ele_cryst",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_2430963126575980498958280
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
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[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb96Te144
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Sb2Te3
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A3B2
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_2519205804608829202414693
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge108Te108
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GeTe
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| null |
[
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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CO_2524304968776184027351519
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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] |
[
3
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2023
|
[
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge144Te144
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GeTe
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| 2024-08-16T15:23:27 |
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|
MD_1394611028200544391469059
| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
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|
CO_2650357190550486331666832
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge18Sb18Te45
|
Ge2Sb2Te5
|
A5B2C2
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PBE
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MD_2127439833971556448789806
| 2024-08-16T14:28:10 |
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PO_1229219790610071906678262
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MD_1833062707632126152463528
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_hex",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_hex",
"refitted_gst_gap_22_pbe_cryst_spc_hex"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_2730321428425228062549909
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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2023
|
[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Sb48
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MD_2127439833971556448789806
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PO_1056427443924281653002273
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MD_2715333019700714844145994
| null |
[
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[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_2788380085404903552746833
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge8Sb16Te32
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GeSb2Te4
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A4B2C
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] | true | null | null | -76,766.858131 | null | 0.495018 | 1.0895 |
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|
MD_2127439833971556448789806
| 2024-08-16T14:52:43 |
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|
PO_1527974318183955225889141
|
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|
MD_8647336201285222360204441
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[
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] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_2852247325261802967033379
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge43Sb44Te108
|
Ge43Sb44Te108
|
A108B44C43
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[
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[
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The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
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[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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MD_2127439833971556448789806
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MD_1137658820933284912488164
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[
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[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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MD_2127439833971556448789806
| 2024-08-16T15:36:33 |
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PO_1191083581836406249843705
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|
MD_6823662732873767234495495
| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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|
CO_3081772558470685346548976
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge36Sb24Te72
|
Ge3Sb2Te6
|
A6B3C2
|
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[
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"DS_r3hav37ufnmb_0"
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CO_3093505670231858061075935
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GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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PO_3100295979487721678412513
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[
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[
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"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_3130247637178911703748329
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GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge80Sb20Te110
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Ge8Sb2Te11
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A11B8C2
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MD_2127439833971556448789806
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PO_2788951941622793782763175
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MD_4550321587241328098297799
| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_3263982200038663979501033
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge72Sb48Te144
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Ge3Sb2Te6
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A6B3C2
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MD_1441031713618345506575407
| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_3280040847786374156324246
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge2
|
Ge
|
A
|
[
32,
32
] |
[
"Ge"
] |
[
1
] | 1 | 2 |
[
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],
[
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],
[
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]
] |
[
[
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],
[
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]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
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| 1 |
CASTEP
|
PBE
| null |
[
[
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0
],
[
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]
] | null |
[
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[
0,
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0
],
[
0,
0,
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]
] | true | null | null | -5,068.014356 | null | 0.10642 | 0.10642 |
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|
MD_2127439833971556448789806
| 2024-08-16T15:08:01 |
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|
PO_4652246833423049042513296
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|
MD_2585776378915383310431038
| null |
[
"main_gst_gap_22_pbesol_dimer",
"extended_gst_gap_22_for_efield_pbesol_dimer",
"refitted_gst_gap_22_pbe_dimer"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:03 |
3333417643827990234571638671856560501513491653958971226983327555116869717426542926237483054602848583663920922501986432169742168129102050140642792630345035
|
CO_3333417643827990234571638
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005
|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge15Sb62Te108
|
Ge15Sb62Te108
|
A108B62C15
|
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true,
true,
true
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[
1,
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| 1 |
CASTEP
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PBE
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PO_6317565642256128338487787
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|
MD_1064850008900002283554905
| null |
[
"main_gst_gap_22_pbesol_cryst_spc_rs",
"extended_gst_gap_22_for_efield_pbesol_cryst_spc_rs",
"refitted_gst_gap_22_pbe_cryst_spc_rs"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
3362249981021280825977635652878956266197415589513960250407148341865062018348796082087793118643980096265706985778543626654880288572219302663775731312224416
|
CO_3362249981021280825977635
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge104Sb26Te144
|
Ge52Sb13Te72
|
A72B52C13
|
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|
MD_5536627955524668390694144
| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
3470077445711018860240924880407164846694192378288961011890938575488980348423682469738228225447016353663377952343260315029370194428154291016094916885751636
|
CO_3470077445711018860240924
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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[
3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb76Te114
|
Sb2Te3
|
A3B2
|
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MD_2127439833971556448789806
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MD_1959771366508673057861790
| null |
[
"main_gst_gap_22_pbesol_iter_1_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
"refitted_gst_gap_22_pbe_iter_1_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_3490515867475126121220578
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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] |
[
3
] |
2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge8Sb16Te32
|
GeSb2Te4
|
A4B2C
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|
MD_5536627955524668390694144
| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
3542661722382684162888903591088923418451587115695752747387008175577411134524107126914794761700315493413185730091587537069106138960548244298068702263085445
|
CO_3542661722382684162888903
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb96Te144
|
Sb2Te3
|
A3B2
|
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| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:06 |
3566870147526587388898054551602826191117262911195259551625990137378755825574657245596693403392400068764954828590539485425004737628153862202203931707237648
|
CO_3566870147526587388898054
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge80Sb20Te110
|
Ge8Sb2Te11
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A11B8C2
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MD_3606526028410192683252523
| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_3617181217706375309995825
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb76Te114
|
Sb2Te3
|
A3B2
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MD_1177975017395951342061739
| null |
[
"main_gst_gap_22_pbesol_iter_4_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_4_domain_iter",
"refitted_gst_gap_22_pbe_iter_4_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_3619460721116547376298927
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
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1,
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3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
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MD_2127439833971556448789806
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{"config-type": "cryst", "masses": [121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76], "sub-config": "MP", "hash": "1318897586789642758477079190647716200173341688336336241375168193942077157810641192336284584754112181590646944747843387115077555662983951543702269007207074", "id": "MD_1318897586789642758477079"}
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[
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"extended_gst_gap_22_for_efield_pbesol_cryst_MP",
"refitted_gst_gap_22_pbe_cryst_MP"
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[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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CO_3619765922571582803671006
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
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"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Sb76Te114
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Sb2Te3
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A3B2
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MD_2127439833971556448789806
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MD_3433370165823589871296004
| null |
[
"main_gst_gap_22_pbesol_iter_3_standard_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_3_standard_iter",
"refitted_gst_gap_22_pbe_iter_3_standard_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_3714332945328066452203566
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Sb72Te108
|
Sb2Te3
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A3B2
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| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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|
CO_3719129355905228664273324
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
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1
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] |
[
3
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2023
|
[
0.23625529319304173,
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] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge36Sb72Te144
|
GeSb2Te4
|
A4B2C
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| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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CO_3757357280039861034318346
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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1,
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3
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2023
|
[
0.23625529319304173,
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge64
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| null |
[
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"extended_gst_gap_22_for_efield_pbesol_cryst_spc_ele_cryst",
"refitted_gst_gap_22_pbe_cryst_spc_ele_cryst"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
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CO_3821701158853459879698637
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
|
[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
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GeSb4Te7
|
A7B4C
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| null |
[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:08 |
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|
CO_3845655913544931512325997
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
|
GeSb4Te7
|
A7B4C
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[
"main_gst_gap_22_pbesol_iter_6_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_6_domain_iter",
"refitted_gst_gap_22_pbe_iter_6_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:09 |
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CO_3860236729900722973572065
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
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3
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2023
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[
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge54Sb36Te108
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Ge3Sb2Te6
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A6B3C2
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MD_6823662732873767234495495
| null |
[
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"extended_gst_gap_22_for_efield_pbesol_iter_1_standard_iter",
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[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:05 |
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CO_3968148707554422888409828
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GST_GAP_22_refitted
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[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
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GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
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Ge4Sb16Te28
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GeSb4Te7
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A7B4C
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MD_2127439833971556448789806
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PO_9381670420624301092522977
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MD_4258300117514373197389689
| null |
[
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[
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CO_4071832902633395912909345
|
GST_GAP_22_refitted
|
[
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"Wei Zhang",
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"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
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{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
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10.60732/164f9a70
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DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge20Sb82Te144
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Ge10Sb41Te72
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A72B41C10
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] | true | null | null | -328,660.075047 | null | 0.677992 | 1.672757 |
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MD_2127439833971556448789806
| 2024-08-16T14:20:51 |
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PO_7332804110158630104721128
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|
MD_5809022839686883334734708
| null |
[
"main_gst_gap_22_pbesol_iter_5_domain_iter",
"extended_gst_gap_22_for_efield_pbesol_iter_5_domain_iter",
"refitted_gst_gap_22_pbe_iter_5_domain_iter"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:07 |
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|
CO_4128197091050899616727646
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
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|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
Ge16Sb64Te112
|
GeSb4Te7
|
A7B4C
|
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] | null |
[
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]
] | true | null | null | -257,293.492271 | null | 0.498172 | 1.410488 |
{"input": {"energy-cutoff": {"value": 600, "units": "eV"}, "pseudopotential": "ultrasoft", "scf-convergence": {"value": 1e-06, "units": "eV"}}, "hash": "2127439833971556448789806564301716500022852324897694830653033720714133886944194651353852313458749271670266058258980838837403038732263847875654974841319928", "id": "MD_2127439833971556448789806"}
|
MD_2127439833971556448789806
| 2024-08-16T15:15:54 |
4242530338926282393780976267002628460957429642235893751535887760671749582962620125819315909166198856293650843974523393412092896553196204236308537905467117
|
PO_4242530338926282393780976
|
{"config-type": "aimd", "masses": [72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 72.63, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 121.76, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6, 127.6], "sub-config": "disorder", "hash": "635248437604434067609015623060890549352218345879182734697206382522581778010191842151436918960043307897262912166286477599899010648944335502521095346736784", "id": "MD_6352484376044340676090156"}
|
MD_6352484376044340676090156
| null |
[
"main_gst_gap_22_pbesol_aimd_disorder",
"extended_gst_gap_22_for_efield_pbesol_aimd_disorder",
"refitted_gst_gap_22_pbe_aimd_disorder"
] |
[
"DS_efcq7a0h6z5w_0",
"DS_jy3ylaf48xg3_0",
"DS_r3hav37ufnmb_0"
] | 2023-12-05T20:21:04 |
4134954212559226566273121824259572407029038175116991299024246183228031647672271784954041827760215103602109144402309040741623821644019136756568700074848336
|
CO_4134954212559226566273121
|
GST_GAP_22_refitted
|
[
"Yuxing Zhou",
"Wei Zhang",
"Evan Ma",
"Volker L. Deringer"
] |
The training dataset for GST_GAP_22, recalculated using the PBE functional. GST-GAP-22 contains configurations of phase-change materials on the quasi-binary GeTe-Sb2Te3 (GST) line of chemical compositions. Data was used for training a machine learning interatomic potential to simulate a range of germanium-antimony-tellurium compositions under realistic device conditions.
|
[
"Ge",
"Sb",
"Te"
] | 3 | 2,692 | 2,692 | 341,004 | 0 | 2,692 | 0 | 2,692 | 0 | 2,692 | -181,554.743129 | 21,020,689,572.877827 | 0 | 2023-12-05T15:21:44 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.23625529319304173,
0.21859567629705223,
0.545149030509906
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1038/s41928-023-01030-x', 'source-data': 'https://doi.org/10.5281/zenodo.8208202', 'other': None}
|
10.60732/164f9a70
|
7636408371583384229363250580186341156197820977251162740756128601401124519097871637753137164096725513451717300230800010388085585915373027919120154892414005
|
DS_jy3ylaf48xg3_0
|
GST_GAP_22_refitted__Zhou-Zhang-Ma-Deringer__DS_jy3ylaf48xg3_0
|
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